Data Products - Week 2 Assignment

Here is my Date:

myTimestamp = Sys.time()

This file was created on 2017-12-14 09:29:38.

Introduction

World Bank collects and makes available the data pertaining to education from all the countries. The data can be easily filtered and downloaded from their easy to use web interface at http://databank.worldbank.org/data/reports.aspx?Code=undefined&id=c755d342&report_name=EdStats_Indicators_Report&populartype=series.

For the purpose of this assignment, I have downloaded educational statistic for all the countries from year 2014 to look at the Net Enrollment Rate for lower secondary school for the female population. This data is used to plot a heat map showing the status for each country where data is available. It was found that the data is available for only 88 countries.

Initialize

setwd("H:\\_R\\Course_9\\Week2\\Proj\\")

library(maps)
library(leaflet)
library(rworldmap)
library(rgeos)
library(rgdal)
library(countrycode)

Data Loading and Scrubbing

## Read file downloaded from WorldBank.
eduData <- read.csv("Data\\Education_Data_WorldBank.csv",na.strings = c("..",""))
names(eduData) <- c("Country","Country_Code","Series","Series_Code","Value")


## Data Scrubbing - Filter NAs
eduData <- eduData[!is.na(eduData$Value),]
eduData$Value <- round(eduData$Value)

head(eduData)

##                Country Country_Code
## 1          Afghanistan          AFG
## 2          Afghanistan          AFG
## 3              Albania          ALB
## 4              Albania          ALB
## 13 Antigua and Barbuda          ATG
## 14 Antigua and Barbuda          ATG
##                                                      Series  Series_Code
## 1  Adjusted net enrolment rate, lower secondary, female (%) UIS.NERA.2.F
## 2    Adjusted net enrolment rate, lower secondary, male (%) UIS.NERA.2.M
## 3  Adjusted net enrolment rate, lower secondary, female (%) UIS.NERA.2.F
## 4    Adjusted net enrolment rate, lower secondary, male (%) UIS.NERA.2.M
## 13 Adjusted net enrolment rate, lower secondary, female (%) UIS.NERA.2.F
## 14   Adjusted net enrolment rate, lower secondary, male (%) UIS.NERA.2.M
##    Value
## 1     38
## 2     63
## 3     88
## 4     89
## 13    81
## 14    74

Transform Data for Map Viewing

## Get Latitude and Longitude for Countries
wmap<- getMap(resolution = "high")
centroids <- gCentroid(wmap,byid=TRUE)
mapData <- as.data.frame(centroids)
mapData <- cbind(rownames(mapData),mapData)
rownames(mapData) <- NULL
names(mapData) <- c("Country", "long","lat")
mapData$Country_Code <- countrycode(sourcevar = mapData$Country,origin="country.name", destination="iso3c")

# Combine the two data sets to fill in centroids in education data
eduDataMerged <- merge(x=eduData, y= mapData, by="Country_Code", all.x=TRUE)

# Lets just look at educational statistics for female population
female_Edu_Data <- eduDataMerged[grep(" female",eduDataMerged$Series),]


# Load the contries polygon data
countries <- readOGR("countriesMap/data/countries.geojson")

## OGR data source with driver: GeoJSON 
## Source: "countriesMap/data/countries.geojson", layer: "countries"
## with 255 features
## It has 2 fields

countryList<-as.data.frame(countries$ISO_A3)
names(countryList)<-c("Country_Code")
countryList$sn<-1:nrow(countryList)


# Plug in education statistics into the contries polegon data.
countryList <- merge (x=countryList,y=female_Edu_Data,by="Country_Code",all.x = TRUE)
countryListRev <- countryList[,c("sn","Country_Code","Value")]
countryListRev <- countryListRev[!duplicated(countryListRev),]
countryListRev <- countryListRev[order(countryListRev$sn),]
countries$edu_Value<-countryListRev$Value

Plot the Map

# Color Palette for the map
pal <- colorNumeric(
  palette = "Blues",
  domain = countries$edu_Value)

# Icon 
bookIcon = icons(iconUrl = "http://cdn.mysitemyway.com/etc-mysitemyway/icons/legacy-previews/icons/3d-transparent-glass-icons-culture/022062-3d-transparent-glass-icon-culture-book2.png",iconWidth = 15, iconHeight = 15)


# Make a map
map <- leaflet(countries)
map %>%
   addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 1,
    color = ~pal(countries$edu_Value)) %>%
   addMarkers(data=female_Edu_Data, icon = bookIcon, label = ~as.character(paste(female_Edu_Data$Country.x," -> ", female_Edu_Data$Value,"%"))) %>%
  addLegend(pal = pal, values = countries$edu_Value, opacity = 1)

## Assuming 'long' and 'lat' are longitude and latitude, respectively